Machine Learning Accelerated Discovery of Promising Thermal Energy Storage Materials with High Heat Capacity

Joshua Ojih, Uche Onyekpe, Alejandro Rodriguez

    Research output: Contribution to journalArticlepeer-review

    8 Citations (Scopus)

    Abstract

    Thermal energy storage offers numerous benefits by reducing energy consumption and promoting the use of renewable energy sources. Thermal energy storage materials have been investigated for many decades with the aim of improving the overall efficiency of energy systems. However, finding solid materials that meet the requirement of high heat capacity has been a grand challenge for material scientists. Herewith, by training various machine learning models on 3377 high-quality data from full density functional theory (DFT) calculations, we efficiently search for potential materials with high heat capacity. We build four traditional machine learning models and two graph neural network models. Cross-comparison of the prediction performance and model accuracy was conducted among different models. The deeperGATGNN model exhibits high prediction accuracy and is used for predicting the heat capacity of 32,026 structures screened from the open quantum material database. We gain deep insight into the correlation between heat capacity and structure descriptors such as space group, prototype, lattice volume, atomic weight, etc. Twenty-two structures were predicted to possess high heat capacity, and the results were further validated with DFT calculations. We also identified one special structure, namely, MnIn2Se4, with space group no. 227 (Fd3¯ m), that exhibits extremely high heat capacity, even higher than that of the Dulong-Petit limit at room temperature. This study paves the way for accelerating the discovery of novel thermal energy storage materials by combining machine learning with minimal DFT inquiry.

    Original languageEnglish
    Pages (from-to)43277-43289
    Number of pages13
    JournalACS applied materials & interfaces
    Volume14
    Issue number38
    Early online date15 Sept 2022
    DOIs
    Publication statusPublished - 28 Sept 2022

    Keywords

    • Dulong-Petit Limit
    • Graph Neural Network
    • Heat Capacity
    • Machine Learning
    • Materials Discovery
    • Thermal Energy Storage

    ASJC Scopus subject areas

    • Materials Science(all)

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